README.md

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language:
- en
pretty_name: SA-FARI
configs:
- config_name: SA-FARI
  data_files:
  - split: train
    path: annotation/sa_fari_train.json
  - split: test
    path: annotation/sa_fari_test.json
license: other
---
# SA-FARI Dataset
**License** CC-BY-NC 4.0

**SA-FARI** is a wildlife camera dataset collected through a collaboration between Meta and [CXL](https://www.conservationxlabs.com/).

All videos and pre-processed JPEGImages can be found in [cxl-public-camera-trap](https://console.cloud.google.com/storage/browser/cxl-public-camera-trap), which contains the following contents:
```
sa_fari/
├── sa_fari_test_tars/
│   ├── JPEGImages_6fps/
│   ├── videos/
├── sa_fari_test/
│   ├── JPEGImages_6fps/
│   ├── videos/
├── sa_fari_train_tars/
│   ├── JPEGImages_6fps/
│   ├── videos/
└── sa_fari_train/
    ├── JPEGImages_6fps/
    └── videos/
```
* `videos`: The original full fps videos.
* `JPEGImages_6fps`: For annotation, the videos have been downsampled to 6fps. This folder contains the downsampled frames compatible with the annotation json files below.

This Hugging Face dataset repo contains the annotations:
```
datasets/facebook/SA-FARI/tree/main/
└── annotation/
    ├── sa_fari_test.json
    ├── sa_fari_test_ext.json
    ├── sa_fari_train.json
    └── sa_fari_train_ext.json
```
* `sa_fari_test.json` and `sa_fari_train.json`
  * Follow the same format as [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/)
* `sa_fari_test_ext.json` and `sa_fari_train_ext.json`
  * In additional to the [SA-Co/VEval] format, we added additional metadata to the following fields:
    * `videos`:
      * `video_num_frames`, `video_fps`, `video_creation_datetime` and `location_id` have been added as additional metadata to the `videos` field.
    * `categories`:
      * `Kingdom`, `Phylum`, `Class`, `Order`, `Family`, `Genus` and `Species` have been added when applicable as additional metadata to the `categories` field.

All the SA-FARI annotation files are compatible to use the visualization notebook and offline evaluator developed in [SAM 3 Github](https://github.com/facebookresearch/sam3/tree/main/scripts/eval/veval).

## Annotation Format
A format breakdown for `sa_fari_test.json` and `sa_fari_train.json`. The format is similar to the [YTVIS](https://youtube-vos.org/dataset/vis/) format.

In the annotation json, e.g. `sa_fari_test.json` there are 5 fields:
* info:
    * A dict containing the dataset info
    * E.g. {'version': 'v1', 'date': '2025-09-24', 'description': 'SA-FARI Test'}
* videos
    * A list of videos that are used in the current annotation json
    * It contains {id, video_name, file_names, height, width, length}
* annotations
    * A list of **positive** masklets and their related info
    * It contains {id, segmentations, bboxes, areas, iscrowd, video_id, height, width, category_id, noun_phrase}
        * video_id should match to the `videos - id` field above
        * category_id should match to the `categories - id` field below
        * segmentations is a list of [RLE](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py)
* categories
    * A **globally** used noun phrase id map, which is true across all 3 domains.
    * It contains {id, name}
        * name is the noun phrase
* video_np_pairs
    * A list of video-np pairs, including both **positive** and **negative** used in the current annotation json
    * It contains {id, video_id, category_id, noun_phrase, num_masklets}
        * video_id should match the `videos - id` above
        * category_id should match the `categories - id` above
        * when `num_masklets > 0` it is a positive video-np pair, and the presenting masklets can be found in the annotations field
        * when `num_masklets = 0` it is a negative video-np pair, meaning no masklet presenting at all
```
data {
    "info": info
    "videos": [video]
    "annotations": [annotation]
    "categories": [category]
    "video_np_pairs": [video_np_pair]
}
video {
    "id": int
    "video_name": str  # e.g. sav_000000
    "file_names": List[str]
    "height": int
    "width": width
    "length": length
}
annotation {
    "id": int
    "segmentations": List[RLE]
    "bboxes": List[List[int, int, int, int]]
    "areas": List[int]
    "iscrowd": int
    "video_id": str
    "height": int
    "width": int
    "category_id": int
    "noun_phrase": str
}
category {
    "id": int
    "name": str
}
video_np_pair {
    "id": int
    "video_id": str
    "category_id": int
    "noun_phrase": str
    "num_masklets" int
}
```